Fix shadowed variable in faiss/utils/NeuralNet.cpp (#3952)

Summary:
Pull Request resolved: #3952

Our upcoming compiler upgrade will require us not to have shadowed variables. Such variables have a _high_ bug rate and reduce readability, so we would like to avoid them even if the compiler was not forcing us to do so.

This codemod attempts to fix an instance of a shadowed variable. Please review with care: if it's failed the result will be a silent bug.

**What's a shadowed variable?**

Shadowed variables are variables in an inner scope with the same name as another variable in an outer scope. Having the same name for both variables might be semantically correct, but it can make the code confusing to read! It can also hide subtle bugs.

This diff fixes such an issue by renaming the variable.

 - If you approve of this diff, please use the "Accept & Ship" button :-)

Reviewed By: asadoughi

Differential Revision: D64398749

fbshipit-source-id: 0e8fd4ab8f6dbf780d4412083fa88fc0df3b89c2

faiss/utils/NeuralNet.cpp

@@ -212,19 +212,19 @@ nn::Int32Tensor2D QINCoStep::encode(
     // repeated codebook
     Tensor2D zqs_r(n * K, d);  // size n, K, d
     Tensor2D cc(n * K, d * 2); // size n, K, d * 2
-    size_t d = this->d;
+    size_t d_2 = this->d;
 
-    auto copy_row = [d](Tensor2D& t, size_t i, size_t j, const float* data) {
+    auto copy_row = [d_2](Tensor2D& t, size_t i, size_t j, const float* data) {
         assert(i <= t.shape[0] && j <= t.shape[1]);
-        memcpy(t.data() + i * t.shape[1] + j, data, sizeof(float) * d);
+        memcpy(t.data() + i * t.shape[1] + j, data, sizeof(float) * d_2);
     };
 
     // manual broadcasting
     for (size_t i = 0; i < n; i++) {
         for (size_t j = 0; j < K; j++) {
-            copy_row(zqs_r, i * K + j, 0, codebook.data() + j * d);
-            copy_row(cc, i * K + j, 0, codebook.data() + j * d);
-            copy_row(cc, i * K + j, d, xhat.data() + i * d);
+            copy_row(zqs_r, i * K + j, 0, codebook.data() + j * d_2);
+            copy_row(cc, i * K + j, 0, codebook.data() + j * d_2);
+            copy_row(cc, i * K + j, d_2, xhat.data() + i * d_2);
         }
     }
 
@@ -237,13 +237,13 @@ nn::Int32Tensor2D QINCoStep::encode(
 
     // add the xhat
     for (size_t i = 0; i < n; i++) {
-        float* zqs_r_row = zqs_r.data() + i * K * d;
-        const float* xhat_row = xhat.data() + i * d;
+        float* zqs_r_row = zqs_r.data() + i * K * d_2;
+        const float* xhat_row = xhat.data() + i * d_2;
         for (size_t l = 0; l < K; l++) {
-            for (size_t j = 0; j < d; j++) {
+            for (size_t j = 0; j < d_2; j++) {
                 zqs_r_row[j] += xhat_row[j];
             }
-            zqs_r_row += d;
+            zqs_r_row += d_2;
         }
     }
 
@@ -252,31 +252,31 @@ nn::Int32Tensor2D QINCoStep::encode(
     float* res = nullptr;
     if (residuals) {
         FAISS_THROW_IF_NOT(
-                residuals->shape[0] == n && residuals->shape[1] == d);
+                residuals->shape[0] == n && residuals->shape[1] == d_2);
         res = residuals->data();
     }
 
     for (size_t i = 0; i < n; i++) {
-        const float* q = x.data() + i * d;
-        const float* db = zqs_r.data() + i * K * d;
+        const float* q = x.data() + i * d_2;
+        const float* db = zqs_r.data() + i * K * d_2;
         float dis_min = HUGE_VALF;
         int64_t idx = -1;
         for (size_t j = 0; j < K; j++) {
-            float dis = fvec_L2sqr(q, db, d);
+            float dis = fvec_L2sqr(q, db, d_2);
             if (dis < dis_min) {
                 dis_min = dis;
                 idx = j;
             }
-            db += d;
+            db += d_2;
         }
         codes.v[i] = idx;
         if (res) {
-            const float* xhat_row = xhat.data() + i * d;
-            const float* xhat_next_row = zqs_r.data() + (i * K + idx) * d;
-            for (size_t j = 0; j < d; j++) {
+            const float* xhat_row = xhat.data() + i * d_2;
+            const float* xhat_next_row = zqs_r.data() + (i * K + idx) * d_2;
+            for (size_t j = 0; j < d_2; j++) {
                 res[j] = xhat_next_row[j] - xhat_row[j];
             }
-            res += d;
+            res += d_2;
         }
     }
     return codes;